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Advanced Battery Management Technologies for Electric Vehicles

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Advanced Battery Management Technologies for Electric Vehicles

Rui Xiong, Weixiang Shen

ISBN: 978-1-119-48168-3 December 2018 280 Pages

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Description

A comprehensive examination of advanced battery management technologies and practices in modern electric vehicles

Policies surrounding energy sustainability and environmental impact have become of increasing interest to governments, industries, and the general public worldwide. Policies embracing strategies that reduce fossil fuel dependency and greenhouse gas emissions have driven the widespread adoption of electric vehicles (EVs), including hybrid electric vehicles (HEVs), pure electric vehicles (PEVs) and plug-in electric vehicles (PHEVs). Battery management systems (BMSs) are crucial components of such vehicles, protecting a battery system from operating outside its Safe Operating Area (SOA), monitoring its working conditions, calculating and reporting its states, and charging and balancing the battery system. Advanced Battery Management Technologies for Electric Vehicles is a compilation of contemporary model-based state estimation methods and battery charging and balancing techniques, providing readers with practical knowledge of both fundamental concepts and practical applications.

This timely and highly-relevant text covers essential areas such as battery modeling and battery state of charge, energy, health and power estimation methods. Clear and accurate background information, relevant case studies, chapter summaries, and reference citations help readers to fully comprehend each topic in a practical context. 

  • Offers up-to-date coverage of modern battery management technology and practice
  • Provides case studies of real-world engineering applications
  • Guides readers from electric vehicle fundamentals to advanced battery management topics
  • Includes chapter introductions and summaries, case studies, and color charts, graphs, and illustrations

Suitable for advanced undergraduate and graduate coursework, Advanced Battery Management Technologies for Electric Vehicles is equally valuable as a reference for professional researchers and engineers.

Biographies xi

Foreword by Professor Sun xiii

Foreword by Professor Ouyang xv

Series Preface xvii

Preface xix

1 Introduction 1

1.1 Background 1

1.2 Electric Vehicle Fundamentals 2

1.3 Requirements for Battery Systems in Electric Vehicles 3

1.3.1 Range Per Charge 4

1.3.2 Acceleration Rate 10

1.3.3 Maximum Speed 11

1.4 Battery Systems 11

1.4.1 Introduction to Electrochemistry of Battery Cells 12

1.4.1.1 Ohmic Overvoltage Drop 14

1.4.1.2 Activation Overvoltage 14

1.4.1.3 Concentration Overvoltage 14

1.4.2 Lead–Acid Batteries 15

1.4.3 NiCd and NiMH Batteries 16

1.4.3.1 NiCd Batteries 16

1.4.3.2 NiMH Batteries 17

1.4.4 Lithium-Ion Batteries 18

1.4.5 Battery Performance Comparison 19

1.4.5.1 Nominal Voltage 20

1.4.5.2 Specific Energy and Energy Density 20

1.4.5.3 Capacity Efficiency and Energy Efficiency 20

1.4.5.4 Specific Power and Power Density 20

1.4.5.5 Self-discharge 21

1.4.5.6 Cycle Life 21

1.4.5.7 Temperature Operation Range 21

1.5 Key Battery Management Technologies 21

1.5.1 Battery Modeling 21

1.5.2 Battery States Estimation 23

1.5.3 Battery Charging 24

1.5.4 Battery Balancing 25

1.6 Battery Management Systems 25

1.6.1 Hardware of BMS 26

1.6.2 Software of BMS 26

1.6.3 Centralized BMS 27

1.6.4 Distributed BMS 28

1.7 Summary 28

References 28

2 BatteryModeling 31

2.1 Background 31

2.2 Electrochemical Models 31

2.3 Black Box Models 33

2.4 Equivalent Circuit Models 34

2.4.1 General n-RC Model 35

2.4.2 Models with Different Numbers of RC Networks 35

2.4.2.1 Rint Model 35

2.4.2.2 Thevenin Model 36

2.4.2.3 Dual Polarization Model 37

2.4.2.4 n-RC Model 38

2.4.3 Open Circuit Voltage 39

2.4.4 Polarization Characteristics 42

2.5 Experiments 43

2.6 Parameter Identification Methods 47

2.6.1 Offline Parameter Identification Method 47

2.6.2 Online Parameter Identification Method 50

2.7 Case Study 51

2.7.1 Testing Data 51

2.7.2 Case One – OFFPIM Application 51

2.7.3 Case Two – ONPIM Application 54

2.7.4 Discussions 56

2.8 Model Uncertainties 57

2.8.1 Battery Aging 57

2.8.2 Battery Type 59

2.8.3 Battery Temperature 61

2.9 Other Battery Models 62

2.10 Summary 64

References 64

3 Battery State of Charge and State of Energy Estimation 67

3.1 Background 67

3.2 Classification 67

3.2.1 Look-Up-Table-Based Method 67

3.2.2 Ampere-Hour Integral Method 68

3.2.3 Data-Driven Estimation Methods 69

3.2.4 Model-Based Estimation Methods 70

3.3 Model-Based SOC Estimation Method with Constant Model Parameters 71

3.3.1 Discrete-Time Realization Algorithm 71

3.3.2 Extended Kalman Filter 72

3.3.2.1 Selection of Correction Coefficients 73

3.3.2.2 SOC Estimation Based on EKF 73

3.3.3 SOC Estimation Based on HIF 75

3.3.4 Case Study 77

3.3.5 Influence of Uncertainties on SOC Estimation 78

3.3.5.1 Initial SOC Value 79

3.3.5.2 Dynamic Working Condition 80

3.3.5.3 Battery Temperature 81

3.4 Model-Based SOC Estimation Method with Identified Model Parameters in Real-Time 84

3.4.1 Real-Time Modeling Process 84

3.4.2 Case Study 86

3.5 Model-Based SOE Estimation Method with Identified Model Parameters in Real-Time 86

3.5.1 SOE Definition 87

3.5.2 State Space Modeling 87

3.5.3 Case Study 88

3.5.4 Influence of Uncertainties on SOE Estimation 89

3.5.4.1 Initial SOE Value 89

3.5.4.2 DynamicWorking Condition 90

3.5.4.3 Battery Temperature 90

3.6 Summary 92

References 92

4 Battery State of Health Estimation 95

4.1 Background 95

4.2 Experimental Methods 95

4.2.1 Direct Measurement Methods 96

4.2.1.1 Capacity or Energy Measurement 96

4.2.1.2 Internal Resistance Measurement 96

4.2.1.3 Impedance Measurement 97

4.2.1.4 Cycle Number Counting 97

4.2.1.5 Destructive Methods 98

4.2.2 Indirect Analysis Methods 98

4.2.2.1 Voltage Trajectory Method 98

4.2.2.2 ICA Method 100

4.2.2.3 DVA Method 102

4.3 Model-Based Methods 104

4.3.1 Adaptive State Estimation Methods 104

4.3.2 Data-Driven Methods 111

4.3.2.1 Empirical and Fitting Methods 112

4.3.2.2 Response Surface-Based Optimization Algorithms 112

4.3.2.3 Sample Entropy Methods 115

4.4 Joint Estimation Method 116

4.4.1 Relationship between SOC and Capacity 116

4.4.2 Case Study 117

4.5 Dual Estimation Method 118

4.5.1 Implementation with the AEKF Algorithm 118

4.5.2 SOC–SOH Estimation 122

4.5.3 Case Study 125

4.6 Summary 128

References 129

5 Battery State of Power Estimation 131

5.1 Background 131

5.2 Instantaneous SOP Estimation Methods 131

5.2.1 HPPC Method 132

5.2.2 SOC-Limited Method 133

5.2.3 Voltage-Limited Method 133

5.2.4 MCD Method 134

5.2.5 Case Study 136

5.3 Continuous SOP Estimation Method 139

5.3.1 Continuous Peak Current Estimation 139

5.3.2 Continuous SOP Estimation 140

5.3.3 Influences of Battery States and Parameters on SOP Estimation 141

5.3.3.1 Uncertainty of SOC 141

5.3.3.2 Case Study 142

5.3.3.3 Uncertainty of Model Parameters 146

5.3.3.4 Case Study 148

5.3.3.5 Uncertainty of SOH 150

5.4 Summary 154

References 154

6 Battery Charging 155

6.1 Background 155

6.2 Basic Terms for Evaluating Charging Performances 157

6.2.1 Cell and Pack 157

6.2.2 Nominal Ampere-Hour Capacity 157

6.2.3 C-rate 157

6.2.4 Cut-off Voltage for Discharge or Charge 157

6.2.5 Cut-off Current 157

6.2.6 State of Charge 158

6.2.7 State of Health 158

6.2.8 Cycle Life 158

6.2.9 Charge Acceptance 158

6.2.10 Ampere-Hour Efficiency 158

6.2.11 Ampere-Hour Charging Factor 159

6.2.12 Energy Efficiency 159

6.2.13 Watt-Hour Charging Factor 159

6.2.14 Trickle Charging 159

6.3 Charging Algorithms for Li-Ion Batteries 159

6.3.1 Constant Current and Constant Voltage Charging 160

6.3.2 Multistep Constant Current Charging 165

6.3.3 Two-Step Constant Current Constant Voltage Charging 168

6.3.4 Constant Voltage Constant Current Constant Voltage Charging 169

6.3.5 Pulse Charging 169

6.3.6 Charging Termination 172

6.3.7 Comparison of Charging Algorithms for Lithium-Ion Batteries 172

6.4 Optimal Charging Current Profiles for Lithium-Ion Batteries 173

6.4.1 Energy Loss Modeling 174

6.4.2 Minimization of Energy Loss 175

6.5 Lithium Titanate Oxide Battery with Extreme Fast Charging Capability 177

6.6 Summary 179

References 180

7 Battery Balancing 183

7.1 Background 183

7.2 Battery Sorting 184

7.2.1 Battery Sorting Based on Capacity and Internal Resistance 184

7.2.2 Battery Sorting Based on a Self-organizing Map 185

7.3 Battery Passive Balancing 189

7.3.1 Fixed Shunt Resistor 189

7.3.2 Switched Shunt Resistor 189

7.3.3 Shunt Transistor 190

7.4 Battery Active Balancing 191

7.4.1 Balancing Criterion 191

7.4.2 Balancing Control 193

7.4.3 Balancing Circuits 193

7.4.3.1 Cell to Cell 194

7.4.3.2 Cell to Pack 196

7.4.3.3 Pack to Cell 199

7.4.3.4 Cell to Energy Storage Tank to Cell 201

7.4.3.5 Cell to Pack to Cell 201

7.5 Battery Active Balancing Systems 203

7.5.1 Active Balancing System Based on the SOC as a Balancing Criterion 204

7.5.1.1 Battery Balancing Criterion 204

7.5.1.2 Battery Balancing Circuit 208

7.5.1.3 Battery Balancing Control 208

7.5.1.4 Experimental Results 208

7.5.2 Active Balancing System Based on FL Controller 212

7.5.2.1 Balancing Principle 215

7.5.2.2 Design of FL Controller 215

7.5.2.3 Adaptability of FL Controller 220

7.5.2.4 Battery Balancing Criterion 222

7.5.2.5 Experimental Results 222

7.6 Summary 227

References 227

8 Battery Management Systems in Electric Vehicles 231

8.1 Background 231

8.2 Battery Management Systems 231

8.2.1 Battery Parameter Acquisition Module 232

8.2.2 Battery System Balancing Module 233

8.2.3 Battery Information Management Module 236

8.2.4 Thermal Management Module 237

8.3 Typical Structure of BMSs 238

8.3.1 Centralized BMS 238

8.3.2 Distributed BMS 239

8.4 Representative Products 239

8.4.1 E-Power BMS 239

8.4.2 Klclear BMS 240

8.4.3 Tesla BMS 241

8.4.4 ICs for BMS Design 242

8.5 Key Points of BMSs in Future Generation 242

8.5.1 Self-Heating Management 243

8.5.2 Safety Management 244

8.5.3 Cloud Computing 244

8.6 Summary 247

References 247

Index 249